Work with Autify Data in Apache Spark Using SQL

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Autify JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Autify test senarios and results.



Access and process Autify Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Autify, Spark can work with live Autify data. This article describes how to connect to and query Autify data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Autify data due to optimized data processing built into the driver. When you issue complex SQL queries to Autify, the driver pushes supported SQL operations, like filters and aggregations, directly to Autify and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Autify data using native data types.

Install the CData JDBC Driver for Autify

Download the CData JDBC Driver for Autify installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Autify Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for Autify JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Autify/lib/cdata.jdbc.autify.jar
  2. With the shell running, you can connect to Autify with a JDBC URL and use the SQL Context load() function to read a table.

    In order to authenticate, you must specify the Autify API Key and Project ID:

    • ApiKey: Log into your account and go to Configurations/Personal Settings and create "New personal access token".
    • ProjectId: ID of the project in your account URL. For ex: If URL is "https://app.autify.com/projects/343/scenarios", 343 is your ProjectId.

    Built-in Connection String Designer

    For assistance in constructing the JDBC URL, use the connection string designer built into the Autify JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

    java -jar cdata.jdbc.autify.jar

    Fill in the connection properties and copy the connection string to the clipboard.

    Configure the connection to Autify, using the connection string generated above.

    scala> val autify_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:autify:ProjectId=255;ApiKey=M9e88D3s31b35347EgNVa;").option("dbtable","Scenarios").option("driver","cdata.jdbc.autify.AutifyDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Autify data as a temporary table:

    scala> autify_df.registerTable("scenarios")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> autify_df.sqlContext.sql("SELECT Name, ProjectURL FROM Scenarios WHERE Id = 46292").collect.foreach(println)

    You will see the results displayed in the console, similar to the following:

Using the CData JDBC Driver for Autify in Apache Spark, you are able to perform fast and complex analytics on Autify data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.